Dynamic environments bring many problems for machine learning models such as data distribution shift, change in task objectives, and environmental interference. The traditional static models without adaptive mechanism tend to have its performance decline. To solve this problem, this paper gives an online updating and adaptive optimization method for dynamic environment. We create an online update architecture which uses sliding windows along with incremental learning so as to perform real time modification of the model according to changes in the data stream. Adaptive learning rate optimization algorithms for different time scales are designed to balance between fast response and long term stable performance. We add meta-learning strategies so we can adjust model parameters dynamically and also do cross-task transfer. We form a self-evolutionary learning framework and mix it up with a reinforced feedback system so our model could self-improve when interacting with its environment. From experimental results using both synthetic and real world data, we show that this approach has better prediction accuracy and stability than online methods as well as lower resource usage. This kind of dynamic learning framework effectively addresses the problem of model degradation in nonstationary environments and provides a scalable theoretical and engineering foundation for the longterm adaptation and continuous evolution of intelligent systems.

Hongyan Qu. Online Update and Adaptive Optimization Algorithm of Machine Learning Decision Model in Dynamic Environment. International Journal of Frontiers in Engineering Technology (2025), Vol. 7, Issue 4: 77-82. https://doi.org/10.25236/IJFET.2025.070411.

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